mathematical-foundations
Latest stories covering mathematical-foundations, with summaries and links to related coverage.
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Mathematics of Data Science
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1 min read
The book "Mathematics of Data Science" covers the mathematical foundations essential for data science applications. Key topics include high-dimensional analysis, singular value decomposition, principal component analysis, linear regression, clustering, and dimension reduction techniques.
The early History of the Singular Value Decomposition (1993) [pdf]
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1 min read
The Singular Value Decomposition (SVD) is a mathematical technique used in linear algebra for decomposing a matrix into singular values and vectors. It has applications in various fields, including statistics, signal processing, and machine learning.
Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models
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16 min read
Richard Bellman's 1952 paper established the foundation for optimal control and reinforcement learning. His later work in the 1950s connected continuous-time systems to a previously published physical result from the 1840s, formulating the optimal condition as a partial differential equation (PDE).